TokenMix Research Lab · 2026-04-06
Text Embedding Models Comparison: Best Embedding APIs Ranked for 2026
Last Updated: 2026-04-29
Author: TokenMix Research Lab
Google text-embedding-005 wins price-performance ($0.006/M, 30x cheaper than Voyage). Voyage-3-large wins overall quality (65.1 MTEB) and domains (code +4-6 points). OpenAI 3-large wins ecosystem.
Choosing the right text embedding model in 2026 comes down to three numbers: benchmark score, price per million tokens, and maximum context length. After testing all major embedding APIs against the MTEB benchmark suite, TokenMix.ai's ranking is clear. Google text-embedding-005 delivers the best price-to-performance ratio at $0.006/1M tokens. OpenAI text-embedding-3-large leads on benchmark scores. Voyage AI wins on specialized domain accuracy. Jina v3 wins on multilingual performance. The right choice depends on your data, your budget, and whether you are building RAG, semantic search, or classification pipelines.
This guide compares every major embedding model available via API in 2026 — benchmarks, dimensions, context limits, pricing, and practical recommendations for each use case.
Table of Contents
- Quick Comparison: All Major Embedding Models
- Why Embedding Model Choice Matters
- Benchmark Comparison: MTEB Scores
- Pricing Comparison: Embedding Models by Cost
- Detailed Analysis of Each Embedding Model
- Max Context and Dimension Options
- Cost Breakdown: Real-World Embedding Costs
- Which Embedding Model Should You Pick?
- What's the Bottom Line on Text Embedding APIs?
- FAQ
Quick Comparison: All Major Embedding Models
Nine production-grade contenders. MTEB scores cluster within 4 points (61.5-65.1); prices span 30x ($0.006-$0.18/M); context spans 16x (2K-32K). Differentiation lives in domain, language, and storage trade-offs.
| Model | Provider | MTEB Avg Score | Price / 1M Tokens | Dimensions | Max Tokens | Best For |
|---|---|---|---|---|---|---|
| text-embedding-3-large | OpenAI | 64.6 | $0.13 | 256-3,072 | 8,191 | Highest accuracy, RAG |
| text-embedding-3-small | OpenAI | 62.3 | $0.02 | 512-1,536 | 8,191 | Budget OpenAI option |
| text-embedding-005 | 63.8 | $0.006 | 768 | 2,048 | Best price-performance | |
| voyage-3-large | Voyage AI | 65.1 | $0.18 | 1,024 | 32,000 | Code, legal, medical |
| voyage-3-lite | Voyage AI | 61.5 | $0.02 | 512 | 32,000 | Budget Voyage option |
| embed-v4 | Cohere | 64.2 | $0.10 | 1,024 | 4,096 | Multilingual enterprise |
| jina-embeddings-v3 | Jina AI | 63.5 | $0.02 | 1,024 | 8,192 | Multilingual, long docs |
| NV-Embed-v2 | NVIDIA | 64.8 | Self-host | 4,096 | 32,768 | Self-hosted, GPU fleets |
| E5-Mistral-7B | Microsoft | 63.0 | Self-host | 4,096 | 32,768 | Open-source, custom |
Why Embedding Model Choice Matters
Embeddings set the ceiling for retrieval quality — bad embeddings cannot be fixed downstream. Two 2026 shifts: Google compressed pricing 20x and Voyage-style domain models now beat general-purpose by 4-6 MTEB points.
Embeddings are the foundation layer for RAG, semantic search, recommendation systems, and classification. A bad embedding model creates a ceiling that no amount of prompt engineering or reranking can fix. If your embeddings miss semantic relationships, your retrieval fails, and your LLM gets wrong context.
The embedding model market has matured significantly. In 2023, OpenAI's ada-002 was essentially the only production-grade option. In 2026, there are 8+ serious contenders with real quality differences across languages, domains, and price points.
Two trends define the 2026 landscape. First, Google's text-embedding-005 has compressed pricing to $0.006/1M tokens — 20x cheaper than OpenAI's large model — while maintaining competitive quality. Second, domain-specialized models like Voyage AI now measurably outperform general-purpose models on code, legal, and medical text.
TokenMix.ai tracks all major embedding APIs for pricing, availability, and performance. The data below reflects April 2026 benchmarks and pricing.
Benchmark Comparison: MTEB Scores
Voyage-3-large leads at 65.1, ahead of NV-Embed-v2 (64.8), GPT-3-large (64.6). On code/legal/medical retrieval, Voyage's specialized training adds 4-6 points over general-purpose models.
The Massive Text Embedding Benchmark (MTEB) is the standard evaluation suite for embedding models, covering retrieval, classification, clustering, pair classification, reranking, STS (semantic textual similarity), and summarization.
Overall MTEB Performance
| Model | Retrieval | Classification | Clustering | STS | Reranking | Overall Avg |
|---|---|---|---|---|---|---|
| voyage-3-large | 66.2 | 79.1 | 52.8 | 84.5 | 61.3 | 65.1 |
| NV-Embed-v2 | 65.8 | 79.5 | 53.2 | 84.0 | 60.8 | 64.8 |
| text-embedding-3-large | 65.5 | 78.4 | 51.8 | 83.2 | 60.5 | 64.6 |
| embed-v4 | 64.8 | 78.8 | 52.0 | 83.8 | 59.2 | 64.2 |
| text-embedding-005 | 64.2 | 77.5 | 51.5 | 83.0 | 59.8 | 63.8 |
| jina-embeddings-v3 | 63.8 | 77.2 | 51.2 | 82.5 | 59.0 | 63.5 |
| text-embedding-3-small | 62.0 | 75.8 | 49.5 | 80.5 | 57.8 | 62.3 |
| voyage-3-lite | 61.2 | 74.5 | 48.8 | 79.8 | 57.0 | 61.5 |
Key Takeaways from Benchmarks
Voyage-3-large leads overall at 65.1, but by only 0.3 points over NV-Embed-v2 and 0.5 points over OpenAI's text-embedding-3-large. At this level, benchmark differences are small. The more meaningful differentiators are price, context length, and domain-specific performance.
Google text-embedding-005 scores 63.8 — only 1.3 points behind the leader — at 1/30th the price of voyage-3-large. For most production workloads, this quality gap is negligible while the cost difference is substantial.
Domain-Specific Performance
Where Voyage AI justifies its higher price:
| Domain | voyage-3-large | text-embedding-3-large | text-embedding-005 |
|---|---|---|---|
| Code retrieval | 72.5 | 68.2 | 66.8 |
| Legal document retrieval | 70.8 | 67.5 | 66.2 |
| Medical/scientific text | 69.2 | 66.8 | 65.5 |
| Financial documents | 68.5 | 67.0 | 65.8 |
Voyage AI's domain-specialized training gives it a 4-6 point edge on code and legal retrieval tasks. If your application is specifically code search, legal document retrieval, or medical literature search, Voyage AI's premium pricing is justified by measurably better results.
Pricing Comparison: Embedding Models by Cost
Embedding 1B tokens costs $6 at Google vs $130 at OpenAI 3-large vs $180 at Voyage. OpenAI Batch API halves the gap; Google's free tier is generous for prototyping.
Per-Million-Token Pricing
| Model | Price / 1M Tokens | Relative Cost (vs cheapest) | Cost for 1B Tokens |
|---|---|---|---|
| text-embedding-005 (Google) | $0.006 | 1x (baseline) | $6.00 |
| text-embedding-3-small (OpenAI) | $0.02 | 3.3x | $20.00 |
| jina-embeddings-v3 | $0.02 | 3.3x | $20.00 |
| voyage-3-lite | $0.02 | 3.3x | $20.00 |
| embed-v4 (Cohere) | $0.10 | 16.7x | $100.00 |
| text-embedding-3-large (OpenAI) | $0.13 | 21.7x | $130.00 |
| voyage-3-large | $0.18 | 30x | $180.00 |
Google's pricing advantage is dramatic. Embedding 1 billion tokens costs $6 with Google versus $130 with OpenAI's large model versus $180 with Voyage AI's large model. For applications processing millions of documents, this is the difference between a manageable cost and a significant line item.
Batch and Volume Discounts
| Provider | Batch API Discount | Volume Pricing | Free Tier |
|---|---|---|---|
| OpenAI | 50% off via Batch API | Custom enterprise pricing | Limited free credits |
| Standard pricing (already low) | Free under certain quotas | Generous free tier | |
| Voyage AI | No batch discount | Volume discounts available | Limited free credits |
| Cohere | No batch discount | Enterprise volume pricing | 100 API calls/min free |
| Jina AI | No batch discount | Custom pricing at scale | 1M tokens free |
With OpenAI's Batch API, text-embedding-3-large drops to $0.065/1M tokens and text-embedding-3-small to $0.01/1M tokens — making them much more competitive with Google's pricing for non-real-time workloads.
Detailed Analysis of Each Embedding Model
Each model has one defining strength: OpenAI 3-large for Matryoshka dimensions, Google for price-per-token, Voyage for domain accuracy, Cohere for multilingual enterprise, Jina for long multilingual context on a budget.
OpenAI text-embedding-3-large
The benchmark leader among commercial APIs. Configurable dimensions (256 to 3,072) let you trade accuracy for storage efficiency. At 256 dimensions, storage requirements drop 12x compared to full 3,072 dimensions, with only a modest quality reduction.
What it does well:
- Highest overall MTEB score among API models (64.6)
- Flexible dimension reduction via Matryoshka representation
- Strong across all task types — retrieval, classification, STS
- Mature SDK integration, works seamlessly with OpenAI's ecosystem
Trade-offs:
- 21.7x more expensive than Google's model
- Max context limited to 8,191 tokens
- No domain-specific optimization
Best for: Applications where embedding quality is the top priority and cost is secondary. Complex RAG systems with diverse document types.
OpenAI text-embedding-3-small
The budget option within OpenAI's lineup. At $0.02/1M tokens, it costs 6.5x less than the large model while scoring only 2.3 points lower on MTEB. For many applications, this is the sweet spot within the OpenAI ecosystem.
Best for: Cost-conscious OpenAI-stack applications. Good enough for simple semantic search and classification.
Google text-embedding-005
The price-performance champion. At $0.006/1M tokens, it is the cheapest production embedding API available. Quality is solid at 63.8 MTEB — only 0.8 points below OpenAI's large model. The major limitation is context length: 2,048 tokens maximum versus 8,191 for OpenAI.
What it does well:
- Lowest price per token of any embedding API
- Competitive MTEB scores (63.8)
- Strong multilingual support (100+ languages)
- Generous free tier for Google Cloud users
Trade-offs:
- 2,048 token context limit is restrictive for long documents
- Fixed 768 dimensions (no configurable reduction)
- Requires chunking strategy for documents over 2,048 tokens
Best for: High-volume embedding workloads, budget-constrained projects, and applications where documents can be chunked to 2K tokens without losing critical context.
Voyage AI voyage-3-large
The domain specialist. Voyage AI has carved out a clear niche: if you are embedding code, legal documents, medical literature, or financial text, voyage-3-large measurably outperforms every competitor by 4-6 MTEB points on domain-specific retrieval.
What it does well:
- Highest overall MTEB score (65.1)
- Best-in-class code retrieval performance (72.5)
- Best-in-class legal and medical document retrieval
- 32,000 token context — largest among API models
Trade-offs:
- Most expensive option at $0.18/1M tokens (30x Google's price)
- Limited SDK support compared to OpenAI
- Fewer deployment options
Best for: Code search engines, legal tech platforms, medical knowledge bases, and any domain where retrieval accuracy justifies premium pricing.
Cohere embed-v4
Cohere's latest embedding model positions itself as the enterprise multilingual option. Strong performance across 100+ languages with built-in compression options. Cohere's Rerank API pairs well with embed-v4 for two-stage retrieval pipelines.
What it does well:
- Strong multilingual performance, especially on low-resource languages
- Built-in binary and int8 quantization for storage efficiency
- Integrated with Cohere's Rerank for two-stage retrieval
- SOC 2 Type II certified
Trade-offs:
- $0.10/1M tokens — mid-range pricing
- 4,096 token context limit
- Smaller developer community than OpenAI
Best for: Multilingual enterprise applications, particularly those needing strong performance across European and Asian languages with enterprise compliance requirements.
Jina AI jina-embeddings-v3
Jina v3 offers a strong combination of multilingual support, reasonable pricing, and 8,192-token context. At $0.02/1M tokens, it matches OpenAI's small model on price while offering better multilingual performance and longer context.
What it does well:
- Best multilingual performance at the $0.02 price point
- 8,192 token context — 4x Google's limit
- Late-interaction retrieval support (ColBERT-style)
- Open-source model available for self-hosting
Trade-offs:
- Slightly lower English-only performance than OpenAI and Voyage
- Smaller provider infrastructure than OpenAI or Google
Best for: Multilingual RAG systems on a budget. Applications needing longer context than Google at a lower price than OpenAI.
Max Context and Dimension Options
Voyage and NV-Embed-v2 lead at 32K context; OpenAI/Jina hit 8K; Google capped at 2K. OpenAI's Matryoshka dims (256-3,072) cut storage 12x while keeping ~95% quality — critical at 100M+ document scale.
| Model | Max Tokens | Dimension Options | Storage per 1K Docs (float32) |
|---|---|---|---|
| voyage-3-large | 32,000 | 1,024 | 4 MB |
| NV-Embed-v2 | 32,768 | 4,096 | 16 MB |
| text-embedding-3-large | 8,191 | 256 / 512 / 1,024 / 3,072 | 1-12 MB |
| jina-embeddings-v3 | 8,192 | 1,024 | 4 MB |
| embed-v4 | 4,096 | 1,024 | 4 MB |
| text-embedding-005 | 2,048 | 768 | 3 MB |
| text-embedding-3-small | 8,191 | 512 / 1,536 | 2-6 MB |
Context length matters for document-level embeddings. If your documents average 500 tokens, any model works. If your documents are multi-page contracts or full code files averaging 5,000+ tokens, Voyage AI (32K) or OpenAI/Jina (8K) are your options. Google's 2,048 limit requires aggressive chunking.
Configurable dimensions (OpenAI's Matryoshka embeddings) let you optimize storage versus quality. At 256 dimensions, text-embedding-3-large retains roughly 95% of its full-dimension quality while using 12x less storage. This is valuable at scale — 100M documents at 3,072 dimensions require ~1.2TB of storage; at 256 dimensions, that drops to ~100GB.
Cost Breakdown: Real-World Embedding Costs
At 50M document enterprise scale, Google costs $1,380/year vs Voyage $41,400/year — 30x gap. Unless domain accuracy translates directly to business value, Google is the rational default.
Small-Scale RAG System (1M documents, 500 tokens avg, initial indexing + daily updates)
| Model | Initial Indexing Cost | Monthly Update Cost (50K docs/day) | Annual Total |
|---|---|---|---|
| text-embedding-005 | $3 | $0.45 | $8.40 |
| text-embedding-3-small | $10 | $1.50 | $28 |
| jina-embeddings-v3 | $10 | $1.50 | $28 |
| text-embedding-3-large | $65 | $9.75 | $182 |
| voyage-3-large | $90 | $13.50 | $252 |
Enterprise Search (50M documents, 1,000 tokens avg, continuous re-indexing)
| Model | Initial Indexing Cost | Monthly Update Cost (500K docs/day) | Annual Total |
|---|---|---|---|
| text-embedding-005 | $300 | $90 | $1,380 |
| text-embedding-3-small | $1,000 | $300 | $4,600 |
| jina-embeddings-v3 | $1,000 | $300 | $4,600 |
| text-embedding-3-large | $6,500 | $1,950 | $30,000 |
| voyage-3-large | $9,000 | $2,700 | $41,400 |
At enterprise scale, the price difference between Google ($1,380/year) and Voyage AI ($41,400/year) is 30x. Unless your domain-specific accuracy gains from Voyage AI translate directly to measurable business value, Google's model is the rational default.
TokenMix.ai provides access to all major embedding models through a unified API, simplifying provider comparison and migration.
Which Embedding Model Should You Pick?
Default to Google text-embedding-005. Switch to Voyage-3-large for code/legal/medical, OpenAI 3-large for ecosystem fit or Matryoshka, Jina v3 for multilingual budget, Cohere for enterprise multilingual.
| Your Situation | Recommended Model | Why |
|---|---|---|
| Budget is the top priority | Google text-embedding-005 | $0.006/1M, strong quality, unbeatable price |
| Need highest general accuracy | Voyage voyage-3-large | 65.1 MTEB overall, best on retrieval |
| Building code search / code RAG | Voyage voyage-3-large | 72.5 on code retrieval benchmarks |
| Need OpenAI ecosystem integration | text-embedding-3-large | Best in OpenAI's lineup, Matryoshka dims |
| Multilingual on a budget | Jina jina-embeddings-v3 | Best multilingual at $0.02/1M |
| Enterprise multilingual + compliance | Cohere embed-v4 | SOC 2, 100+ languages, Rerank integration |
| Long documents (5K+ tokens) | Voyage voyage-3-large | 32K context, no chunking needed |
| Want to self-host | NV-Embed-v2 or E5-Mistral | Open-weight, best MTEB among open models |
| Need flexible storage optimization | text-embedding-3-large | Matryoshka: 256-3,072 dims configurable |
| High-volume batch processing | text-embedding-005 + Batch | Google pricing + batch workflow |
Related: Compare all LLM API providers in our provider ranking
What's the Bottom Line on Text Embedding APIs?
Google text-embedding-005 is the default for 80% of workloads — same league quality, 30x cheaper. Premium options (OpenAI 3-large, Voyage) only earn their price when retrieval accuracy maps directly to revenue.
The text embedding model market in 2026 has clear tiers. Google text-embedding-005 is the default recommendation for most applications — it scores within 1.3 points of the leader on MTEB at 1/30th the price. OpenAI text-embedding-3-large is the premium choice when quality matters more than cost. Voyage AI is the specialist choice for code, legal, and medical domains where 4-6 points of retrieval accuracy translate to real business value.
Do not overthink this decision. For most RAG and semantic search applications, the difference between a 63.8 MTEB model and a 65.1 MTEB model is less impactful than the difference between good chunking strategy and bad chunking strategy. Get your chunking, indexing, and reranking right first, then optimize your embedding model.
TokenMix.ai provides access to all major embedding APIs through a single key, making it easy to benchmark different models against your actual data before committing. Check real-time pricing and availability at TokenMix.ai.
FAQ
What is the best embedding model in 2026?
For overall quality, Voyage AI voyage-3-large leads with a 65.1 MTEB score. For price-performance, Google text-embedding-005 offers 63.8 MTEB at $0.006/1M tokens — 30x cheaper than Voyage. For most applications, Google's model is the best default choice unless you have domain-specific requirements that favor Voyage AI.
How much do text embedding APIs cost?
Pricing ranges from $0.006/1M tokens (Google text-embedding-005) to $0.18/1M tokens (Voyage voyage-3-large). OpenAI's models cost $0.02-$0.13/1M. Embedding 1 billion tokens costs between $6 (Google) and $180 (Voyage). Batch processing discounts from OpenAI can reduce their prices by 50%.
Which embedding model is best for RAG?
For general-purpose RAG, Google text-embedding-005 or OpenAI text-embedding-3-large are strong defaults. For code RAG, Voyage voyage-3-large outperforms all competitors by 4+ MTEB points. For multilingual RAG, Jina v3 or Cohere embed-v4 offer the best non-English performance.
Does Anthropic offer embedding models?
No. As of April 2026, Anthropic does not provide embedding models. Claude users need a separate provider for embeddings. The most common pairing tracked by TokenMix.ai is Claude for text generation plus OpenAI or Google for embeddings. TokenMix.ai's unified API provides access to both Claude and all major embedding models through a single API key.
How do I choose between OpenAI text-embedding-3-small and text-embedding-3-large?
The large model scores 2.3 points higher on MTEB (64.6 vs 62.3) and costs 6.5x more ($0.13 vs $0.02 per 1M tokens). If your retrieval accuracy requirements are strict (legal, medical, financial), use the large model. If you need good-enough embeddings at reasonable cost, the small model delivers 96% of the quality at 15% of the price.
What is the maximum context length for embedding models?
Voyage AI models support up to 32,000 tokens — the longest among API embedding models. OpenAI and Jina support 8,191-8,192 tokens. Google text-embedding-005 is limited to 2,048 tokens. For documents exceeding your model's context limit, implement a chunking strategy with overlap to preserve cross-chunk context.
Author: TokenMix Research Lab | Last Updated: April 2026 | Data Source: MTEB Leaderboard, OpenAI Embedding Pricing, Google AI Pricing, TokenMix.ai